To evaluate the SELD performance, we used the official SELD evaluation metrics [1]} from the DCASE 2021 Challenge. The metrics not only jointly evaluate SED and DOAE, but also take into account the cases where multiple instances of the same class overlap. The SELD evaluation metrics consist of location-dependent error rate (\(\text{ER}_{\le T}\) ) and F1 score (\(\text{F}_{\le T}\) ) for SED; and class-dependent localization error (\(\text{LE}_\text{CD}\) ), and localization recall (\(\text{LR}_\text{CD}\) ) for DOAE. A sound event is considered a correct detection only if it has a correct class prediction and its estimated DOA is also less than \(T\)  away from the DOA ground truth, where \(T={20}{}\)  for the official challenge. The DOAE metrics are also class-dependent, that is, the detected DOA is only counted if its corresponding detected sound class is correct. A good SELD system should have low \(\text{ER}_{\le T}\) , high \(\text{F}_{\le T}\) , low \(\text{LE}_\text{CD}\) , and high \(\text{LR}_\text{CD}\) .
